Zobrazeno 1 - 10
of 242
pro vyhledávání: '"Cao, Liyuan"'
Parameter-efficient fine-tuning (PEFT) significantly reduces memory costs when adapting large language models (LLMs) for downstream applications. However, traditional first-order (FO) fine-tuning algorithms incur substantial memory overhead due to th
Externí odkaz:
http://arxiv.org/abs/2410.07698
Partial model personalization, which encompasses both shared and personal variables in its formulation, is a critical optimization problem in federated learning. It balances individual client needs with collective knowledge utilization, and serves as
Externí odkaz:
http://arxiv.org/abs/2309.17409
We study in this paper the function approximation error of multivariate linear extrapolation. While the sharp error bound of linear interpolation already exists in the literature, linear extrapolation is used far more often in applications such as de
Externí odkaz:
http://arxiv.org/abs/2307.00358
We study in this paper the function approximation error of linear interpolation and extrapolation. Several upper bounds are presented along with the conditions under which they are sharp. All results are under the assumptions that the function has Li
Externí odkaz:
http://arxiv.org/abs/2209.12606
In this paper, we present convergence guarantees for a modified trust-region method designed for minimizing objective functions whose value and gradient and Hessian estimates are computed with noise. These estimates are produced by generic stochastic
Externí odkaz:
http://arxiv.org/abs/2205.03667
Publikováno v:
In Energy Economics November 2024 139
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In Engineering Structures 15 September 2024 315
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In Soil Dynamics and Earthquake Engineering January 2025 188 Part A
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In Construction and Building Materials 26 July 2024 437
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In Journal of Wind Engineering & Industrial Aerodynamics April 2024 247